We just learned what autoencoders are in the previous section. We learned about a vanilla autoencoder, which is basically the feedforward shallow network with one hidden layer. Instead of keeping them as a feedforward network, can we use them as a convolutional network? Since we know that a convolutional network is good at classifying and recognizing images (provided that we use convolutional layers instead of feedforward layers in the autoencoders), it will learn to reconstruct the inputs better when the inputs are images.
Thus, we introduce a new type of autoencoders called CAEs that use a convolutional network instead of a vanilla neural network. In the vanilla autoencoders, encoders and decoders are basically a feedforward network. But in CAEs, they are basically convolutional networks. This means the encoder consists of convolutional layers...
